Abstract
Network-based similarity measures have found wide applications in recommendation algorithms and made significant contributions for uncovering users’ potential interests. However, existing measures are generally biased in terms of popularity, that the popular objects tend to have more common neighbours with others and thus are considered more similar to others. Such popularity bias of similarity quantification will result in the biased recommendations, with either poor accuracy or poor diversity. Based on the bipartite network modelling of the user-object interactions, this paper firstly calculates the expected number of common neighbours of two objects with given popularities in random networks. A Balanced Common Neighbour similarity index is accordingly developed by removing the random-driven common neighbours, estimated as the expected number, from the total number. Recommendation experiments in three data sets show that balancing the popularity bias in a certain degree can significantly improve the recommendations’ accuracy and diversity simultaneously.
Highlights
The overwhelming online information, though provides users massive and diverse choices, is making it more and more difficult to find what they really want
In this paper we develop a Balanced Common Neighbour (BCN) index for measuring the object similarity in bipartite networks based on the evaluation of the expected common neighbourhood for two objects with specific popularities
Countless valuable niche information is hidden in the dominance of popular information
Summary
The overwhelming online information, though provides users massive and diverse choices, is making it more and more difficult to find what they really want. Allocation (RA) index [18] take the user degree as weight to the CN index and read sAαβA = i∈Γα∩Γβ 1/ log(ki) and sRαβA = i∈Γα∩Γβ 1/ki respectively. Though have been widely applied in both analytics of complex networks and the recommender systems, most of the existing similarity indices have systematic popularity bias [12,20,21]. In this paper we develop a Balanced Common Neighbour (BCN) index for measuring the object similarity in bipartite networks based on the evaluation of the expected common neighbourhood for two objects with specific popularities. Applying the proposed method in personalised recommendation, we show that to balance the popularity bias of similarities in a certain degree can largely improve the performances of the recommendations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
